Overview

Dataset statistics

Number of variables21
Number of observations3649
Missing cells6978
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory332.7 B

Variable types

Text3
Numeric18

Alerts

Access to clean fuels for cooking is highly overall correlated with Access to electricity (% of population) and 6 other fieldsHigh correlation
Access to electricity (% of population) is highly overall correlated with Access to clean fuels for cooking and 6 other fieldsHigh correlation
Electricity from fossil fuels (TWh) is highly overall correlated with Access to clean fuels for cooking and 4 other fieldsHigh correlation
Electricity from nuclear (TWh) is highly overall correlated with Value_co2_emissions_kt_by_countryHigh correlation
Electricity from renewables (TWh) is highly overall correlated with Electricity from fossil fuels (TWh) and 4 other fieldsHigh correlation
Latitude is highly overall correlated with Access to clean fuels for cooking and 1 other fieldsHigh correlation
Low-carbon electricity (% electricity) is highly overall correlated with Electricity from renewables (TWh) and 3 other fieldsHigh correlation
Primary energy consumption per capita (kWh/person) is highly overall correlated with Access to clean fuels for cooking and 5 other fieldsHigh correlation
Renewable energy share in the total final energy consumption (%) is highly overall correlated with Access to clean fuels for cooking and 5 other fieldsHigh correlation
Renewable-electricity-generating-capacity-per-capita is highly overall correlated with Electricity from renewables (TWh) and 2 other fieldsHigh correlation
Renewables (% equivalent primary energy) is highly overall correlated with Electricity from renewables (TWh) and 3 other fieldsHigh correlation
Value_co2_emissions_kt_by_country is highly overall correlated with Access to clean fuels for cooking and 5 other fieldsHigh correlation
gdp_per_capita is highly overall correlated with Access to clean fuels for cooking and 3 other fieldsHigh correlation
Access to clean fuels for cooking has 169 (4.6%) missing valuesMissing
Renewable-electricity-generating-capacity-per-capita has 931 (25.5%) missing valuesMissing
Financial flows to developing countries (US $) has 2089 (57.2%) missing valuesMissing
Renewable energy share in the total final energy consumption (%) has 194 (5.3%) missing valuesMissing
Electricity from nuclear (TWh) has 126 (3.5%) missing valuesMissing
Low-carbon electricity (% electricity) has 42 (1.2%) missing valuesMissing
Energy intensity level of primary energy (MJ/$2017 PPP GDP) has 207 (5.7%) missing valuesMissing
Value_co2_emissions_kt_by_country has 428 (11.7%) missing valuesMissing
Renewables (% equivalent primary energy) has 2137 (58.6%) missing valuesMissing
gdp_growth has 317 (8.7%) missing valuesMissing
gdp_per_capita has 282 (7.7%) missing valuesMissing
Renewable-electricity-generating-capacity-per-capita has 229 (6.3%) zerosZeros
Renewable energy share in the total final energy consumption (%) has 95 (2.6%) zerosZeros
Electricity from fossil fuels (TWh) has 141 (3.9%) zerosZeros
Electricity from nuclear (TWh) has 2945 (80.7%) zerosZeros
Electricity from renewables (TWh) has 639 (17.5%) zerosZeros
Low-carbon electricity (% electricity) has 618 (16.9%) zerosZeros
Renewables (% equivalent primary energy) has 58 (1.6%) zerosZeros

Reproduction

Analysis started2024-05-03 15:04:39.020205
Analysis finished2024-05-03 15:05:30.697949
Duration51.68 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Entity
Text

Distinct176
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size233.1 KiB
2024-05-03T20:35:30.972247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length32
Median length22
Mean length8.3762675
Min length4

Characters and Unicode

Total characters30565
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
and 126
 
2.8%
guinea 63
 
1.4%
new 63
 
1.4%
united 63
 
1.4%
saint 63
 
1.4%
islands 42
 
0.9%
republic 42
 
0.9%
sudan 29
 
0.6%
south 29
 
0.6%
antigua 21
 
0.5%
Other values (191) 3957
88.0%
2024-05-03T20:35:31.414034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4917
16.1%
i 2766
 
9.0%
n 2474
 
8.1%
e 1975
 
6.5%
r 1667
 
5.5%
o 1485
 
4.9%
u 1256
 
4.1%
t 1177
 
3.9%
l 1029
 
3.4%
d 995
 
3.3%
Other values (42) 10824
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25323
82.8%
Uppercase Letter 4372
 
14.3%
Space Separator 849
 
2.8%
Dash Punctuation 21
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4917
19.4%
i 2766
10.9%
n 2474
9.8%
e 1975
 
7.8%
r 1667
 
6.6%
o 1485
 
5.9%
u 1256
 
5.0%
t 1177
 
4.6%
l 1029
 
4.1%
d 995
 
3.9%
Other values (16) 5582
22.0%
Uppercase Letter
ValueCountFrequency (%)
S 513
11.7%
B 378
 
8.6%
C 357
 
8.2%
A 315
 
7.2%
M 308
 
7.0%
G 295
 
6.7%
N 273
 
6.2%
T 231
 
5.3%
L 210
 
4.8%
P 210
 
4.8%
Other values (14) 1282
29.3%
Space Separator
ValueCountFrequency (%)
849
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29695
97.2%
Common 870
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4917
16.6%
i 2766
 
9.3%
n 2474
 
8.3%
e 1975
 
6.7%
r 1667
 
5.6%
o 1485
 
5.0%
u 1256
 
4.2%
t 1177
 
4.0%
l 1029
 
3.5%
d 995
 
3.4%
Other values (40) 9954
33.5%
Common
ValueCountFrequency (%)
849
97.6%
- 21
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30565
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4917
16.1%
i 2766
 
9.0%
n 2474
 
8.1%
e 1975
 
6.5%
r 1667
 
5.5%
o 1485
 
4.9%
u 1256
 
4.1%
t 1177
 
3.9%
l 1029
 
3.4%
d 995
 
3.3%
Other values (42) 10824
35.4%

Year
Real number (ℝ)

Distinct21
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.0384
Minimum2000
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:31.566026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12005
median2010
Q32015
95-th percentile2019
Maximum2020
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0542284
Coefficient of variation (CV)0.0030119964
Kurtosis-1.2038433
Mean2010.0384
Median Absolute Deviation (MAD)5
Skewness-0.0091418711
Sum7334630
Variance36.653681
MonotonicityNot monotonic
2024-05-03T20:35:31.682893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2020 175
 
4.8%
2019 175
 
4.8%
2018 175
 
4.8%
2017 175
 
4.8%
2016 175
 
4.8%
2015 175
 
4.8%
2014 175
 
4.8%
2013 175
 
4.8%
2011 174
 
4.8%
2012 174
 
4.8%
Other values (11) 1901
52.1%
ValueCountFrequency (%)
2000 173
4.7%
2001 172
4.7%
2002 172
4.7%
2003 172
4.7%
2004 172
4.7%
2005 172
4.7%
2006 172
4.7%
2007 174
4.8%
2008 174
4.8%
2009 174
4.8%
ValueCountFrequency (%)
2020 175
4.8%
2019 175
4.8%
2018 175
4.8%
2017 175
4.8%
2016 175
4.8%
2015 175
4.8%
2014 175
4.8%
2013 175
4.8%
2012 174
4.8%
2011 174
4.8%

Access to electricity (% of population)
Real number (ℝ)

HIGH CORRELATION 

Distinct2040
Distinct (%)56.1%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean78.933702
Minimum1.2522693
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:31.825871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.2522693
5-th percentile12.704053
Q159.80089
median98.36157
Q3100
95-th percentile100
Maximum100
Range98.747731
Interquartile range (IQR)40.19911

Descriptive statistics

Standard deviation30.275541
Coefficient of variation (CV)0.38355659
Kurtosis-0.035819042
Mean78.933702
Median Absolute Deviation (MAD)1.63843
Skewness-1.2058453
Sum287239.74
Variance916.60841
MonotonicityNot monotonic
2024-05-03T20:35:31.973947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1355
37.1%
99.8 21
 
0.6%
99.9 20
 
0.5%
99.7 18
 
0.5%
99 14
 
0.4%
99.5 13
 
0.4%
99.3 10
 
0.3%
99.6 9
 
0.2%
99.4 8
 
0.2%
97.7 7
 
0.2%
Other values (2030) 2164
59.3%
(Missing) 10
 
0.3%
ValueCountFrequency (%)
1.2522693 1
< 0.1%
1.2537057 1
< 0.1%
1.2792896 1
< 0.1%
1.613591 1
< 0.1%
1.8925012 1
< 0.1%
1.9 1
< 0.1%
2.4632368 1
< 0.1%
2.5914624 1
< 0.1%
2.66 1
< 0.1%
2.79742 1
< 0.1%
ValueCountFrequency (%)
100 1355
37.1%
99.99887 1
 
< 0.1%
99.99799 1
 
< 0.1%
99.99674 1
 
< 0.1%
99.996445 1
 
< 0.1%
99.99625 1
 
< 0.1%
99.99448 1
 
< 0.1%
99.99224 1
 
< 0.1%
99.992004 1
 
< 0.1%
99.9899 1
 
< 0.1%

Access to clean fuels for cooking
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct896
Distinct (%)25.7%
Missing169
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean63.255287
Minimum0
Maximum100
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:32.224925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9
Q123.175
median83.15
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)76.825

Descriptive statistics

Standard deviation39.043658
Coefficient of variation (CV)0.61723943
Kurtosis-1.4192375
Mean63.255287
Median Absolute Deviation (MAD)16.85
Skewness-0.50805448
Sum220128.4
Variance1524.4072
MonotonicityNot monotonic
2024-05-03T20:35:32.371706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1071
29.4%
0.9 36
 
1.0%
99.9 32
 
0.9%
0.7 26
 
0.7%
0.6 25
 
0.7%
0.8 25
 
0.7%
1.1 25
 
0.7%
0.4 24
 
0.7%
1 23
 
0.6%
0.2 23
 
0.6%
Other values (886) 2170
59.5%
(Missing) 169
 
4.6%
ValueCountFrequency (%)
0 8
 
0.2%
0.1 2
 
0.1%
0.2 23
0.6%
0.3 18
0.5%
0.4 24
0.7%
0.5 19
0.5%
0.6 25
0.7%
0.65 1
 
< 0.1%
0.7 26
0.7%
0.8 25
0.7%
ValueCountFrequency (%)
100 1071
29.4%
99.9 32
 
0.9%
99.8 19
 
0.5%
99.7 10
 
0.3%
99.65 1
 
< 0.1%
99.6 9
 
0.2%
99.55 1
 
< 0.1%
99.5 7
 
0.2%
99.4 7
 
0.2%
99.3 3
 
0.1%

Renewable-electricity-generating-capacity-per-capita
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct2110
Distinct (%)77.6%
Missing931
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean113.1375
Minimum0
Maximum3060.19
Zeros229
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:32.522393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.54
median32.91
Q3112.21
95-th percentile541.8485
Maximum3060.19
Range3060.19
Interquartile range (IQR)108.67

Descriptive statistics

Standard deviation244.16726
Coefficient of variation (CV)2.1581461
Kurtosis40.450249
Mean113.1375
Median Absolute Deviation (MAD)32.2
Skewness5.3669329
Sum307507.72
Variance59617.649
MonotonicityNot monotonic
2024-05-03T20:35:32.665472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 229
 
6.3%
0.01 11
 
0.3%
0.06 11
 
0.3%
0.26 10
 
0.3%
0.05 10
 
0.3%
0.17 9
 
0.2%
0.04 8
 
0.2%
0.22 8
 
0.2%
0.07 7
 
0.2%
0.2 6
 
0.2%
Other values (2100) 2409
66.0%
(Missing) 931
 
25.5%
ValueCountFrequency (%)
0 229
6.3%
0.01 11
 
0.3%
0.02 4
 
0.1%
0.04 8
 
0.2%
0.05 10
 
0.3%
0.06 11
 
0.3%
0.07 7
 
0.2%
0.08 5
 
0.1%
0.09 2
 
0.1%
0.1 1
 
< 0.1%
ValueCountFrequency (%)
3060.19 1
< 0.1%
3026.4 1
< 0.1%
2238.69 1
< 0.1%
2218.72 1
< 0.1%
2216.23 1
< 0.1%
2194.18 1
< 0.1%
2192.15 1
< 0.1%
2171.22 1
< 0.1%
2166.11 1
< 0.1%
2146.8 1
< 0.1%
Distinct1017
Distinct (%)65.2%
Missing2089
Missing (%)57.2%
Infinite0
Infinite (%)0.0%
Mean94224000
Minimum0
Maximum5.20231 × 109
Zeros25
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:32.816972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10000
Q1260000
median5665000
Q355347500
95-th percentile4.457835 × 108
Maximum5.20231 × 109
Range5.20231 × 109
Interquartile range (IQR)55087500

Descriptive statistics

Standard deviation2.9815441 × 108
Coefficient of variation (CV)3.1643149
Kurtosis102.36703
Mean94224000
Median Absolute Deviation (MAD)5645000
Skewness8.3882518
Sum1.4698944 × 1011
Variance8.889605 × 1016
MonotonicityNot monotonic
2024-05-03T20:35:32.970372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 65
 
1.8%
20000 51
 
1.4%
30000 30
 
0.8%
0 25
 
0.7%
50000 22
 
0.6%
40000 21
 
0.6%
90000 19
 
0.5%
120000 14
 
0.4%
60000 14
 
0.4%
100000 13
 
0.4%
Other values (1007) 1286
35.2%
(Missing) 2089
57.2%
ValueCountFrequency (%)
0 25
 
0.7%
10000 65
1.8%
20000 51
1.4%
30000 30
0.8%
40000 21
 
0.6%
50000 22
 
0.6%
60000 14
 
0.4%
70000 12
 
0.3%
80000 13
 
0.4%
90000 19
 
0.5%
ValueCountFrequency (%)
5202310000 1
< 0.1%
4284370000 1
< 0.1%
3386850000 1
< 0.1%
2811680000 1
< 0.1%
2472850000 1
< 0.1%
2112780000 1
< 0.1%
2070240000 1
< 0.1%
1950940000 1
< 0.1%
1791180000 1
< 0.1%
1764130000 1
< 0.1%

Renewable energy share in the total final energy consumption (%)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct2587
Distinct (%)74.9%
Missing194
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean32.638165
Minimum0
Maximum96.04
Zeros95
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:33.124170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.077
Q16.515
median23.3
Q355.245
95-th percentile88.113
Maximum96.04
Range96.04
Interquartile range (IQR)48.73

Descriptive statistics

Standard deviation29.894901
Coefficient of variation (CV)0.91594921
Kurtosis-0.90581772
Mean32.638165
Median Absolute Deviation (MAD)20.25
Skewness0.67087283
Sum112764.86
Variance893.70513
MonotonicityNot monotonic
2024-05-03T20:35:33.269877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95
 
2.6%
0.01 32
 
0.9%
0.07 13
 
0.4%
0.05 10
 
0.3%
0.06 10
 
0.3%
0.08 10
 
0.3%
0.43 8
 
0.2%
0.19 7
 
0.2%
0.18 7
 
0.2%
0.11 7
 
0.2%
Other values (2577) 3256
89.2%
(Missing) 194
 
5.3%
ValueCountFrequency (%)
0 95
2.6%
0.01 32
 
0.9%
0.02 4
 
0.1%
0.03 5
 
0.1%
0.04 4
 
0.1%
0.05 10
 
0.3%
0.06 10
 
0.3%
0.07 13
 
0.4%
0.08 10
 
0.3%
0.09 4
 
0.1%
ValueCountFrequency (%)
96.04 1
< 0.1%
96.01 1
< 0.1%
95.76 1
< 0.1%
95.55 1
< 0.1%
95.35 1
< 0.1%
95.31 1
< 0.1%
95.29 1
< 0.1%
95.28 1
< 0.1%
95.22 1
< 0.1%
95.18 1
< 0.1%

Electricity from fossil fuels (TWh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1859
Distinct (%)51.2%
Missing21
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean70.365003
Minimum0
Maximum5184.13
Zeros141
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:33.414111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.29
median2.97
Q326.8375
95-th percentile208.1345
Maximum5184.13
Range5184.13
Interquartile range (IQR)26.5475

Descriptive statistics

Standard deviation348.05187
Coefficient of variation (CV)4.9463775
Kurtosis99.869361
Mean70.365003
Median Absolute Deviation (MAD)2.93
Skewness9.3967603
Sum255284.23
Variance121140.1
MonotonicityNot monotonic
2024-05-03T20:35:33.564104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 141
 
3.9%
0.05 68
 
1.9%
0.04 67
 
1.8%
0.06 66
 
1.8%
0.03 56
 
1.5%
0.02 48
 
1.3%
0.08 35
 
1.0%
0.2 31
 
0.8%
0.07 28
 
0.8%
0.09 27
 
0.7%
Other values (1849) 3061
83.9%
ValueCountFrequency (%)
0 141
3.9%
0.01 27
 
0.7%
0.02 48
 
1.3%
0.03 56
 
1.5%
0.04 67
1.8%
0.05 68
1.9%
0.06 66
1.8%
0.07 28
 
0.8%
0.08 35
 
1.0%
0.09 27
 
0.7%
ValueCountFrequency (%)
5184.13 1
< 0.1%
5098.22 1
< 0.1%
4990.28 1
< 0.1%
4643.1 1
< 0.1%
4355 1
< 0.1%
4345.86 1
< 0.1%
4222.76 1
< 0.1%
4203.77 1
< 0.1%
3869.38 1
< 0.1%
3811.77 1
< 0.1%

Electricity from nuclear (TWh)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct547
Distinct (%)15.5%
Missing126
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean13.45019
Minimum0
Maximum809.41
Zeros2945
Zeros (%)80.7%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:33.715117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile58.345
Maximum809.41
Range809.41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation73.006623
Coefficient of variation (CV)5.427925
Kurtosis80.982202
Mean13.45019
Median Absolute Deviation (MAD)0
Skewness8.5651977
Sum47385.02
Variance5329.967
MonotonicityNot monotonic
2024-05-03T20:35:33.863227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2945
80.7%
5.55 3
 
0.1%
3.91 3
 
0.1%
11.62 3
 
0.1%
5.53 2
 
0.1%
5.66 2
 
0.1%
70.34 2
 
0.1%
2.27 2
 
0.1%
2.29 2
 
0.1%
11.38 2
 
0.1%
Other values (537) 557
 
15.3%
(Missing) 126
 
3.5%
ValueCountFrequency (%)
0 2945
80.7%
0.34 1
 
< 0.1%
0.38 1
 
< 0.1%
1.56 1
 
< 0.1%
1.74 1
 
< 0.1%
1.8 1
 
< 0.1%
1.81 1
 
< 0.1%
1.82 1
 
< 0.1%
1.84 1
 
< 0.1%
1.9 1
 
< 0.1%
ValueCountFrequency (%)
809.41 1
< 0.1%
807.08 1
< 0.1%
806.97 1
< 0.1%
806.42 1
< 0.1%
806.21 1
< 0.1%
805.69 1
< 0.1%
804.95 1
< 0.1%
798.85 1
< 0.1%
797.18 1
< 0.1%
797.17 1
< 0.1%

Electricity from renewables (TWh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1533
Distinct (%)42.3%
Missing21
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean23.96801
Minimum0
Maximum2184.94
Zeros639
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:34.019473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.04
median1.47
Q39.6
95-th percentile98.6575
Maximum2184.94
Range2184.94
Interquartile range (IQR)9.56

Descriptive statistics

Standard deviation104.43108
Coefficient of variation (CV)4.3571029
Kurtosis166.04063
Mean23.96801
Median Absolute Deviation (MAD)1.47
Skewness11.057606
Sum86955.94
Variance10905.852
MonotonicityNot monotonic
2024-05-03T20:35:34.160067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 639
 
17.5%
0.01 134
 
3.7%
0.03 55
 
1.5%
0.02 54
 
1.5%
0.05 37
 
1.0%
0.04 27
 
0.7%
0.15 26
 
0.7%
0.06 26
 
0.7%
0.14 24
 
0.7%
0.09 21
 
0.6%
Other values (1523) 2585
70.8%
(Missing) 21
 
0.6%
ValueCountFrequency (%)
0 639
17.5%
0.01 134
 
3.7%
0.02 54
 
1.5%
0.03 55
 
1.5%
0.04 27
 
0.7%
0.05 37
 
1.0%
0.06 26
 
0.7%
0.07 18
 
0.5%
0.08 17
 
0.5%
0.09 21
 
0.6%
ValueCountFrequency (%)
2184.94 1
< 0.1%
2014.57 1
< 0.1%
1835.32 1
< 0.1%
1667.06 1
< 0.1%
1522.79 1
< 0.1%
1393.66 1
< 0.1%
1289.23 1
< 0.1%
1093.37 1
< 0.1%
999.56 1
< 0.1%
821.4 1
< 0.1%

Low-carbon electricity (% electricity)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct2647
Distinct (%)73.4%
Missing42
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean36.801182
Minimum0
Maximum100.00001
Zeros618
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:34.407147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.8778469
median27.865068
Q364.403792
95-th percentile99.432451
Maximum100.00001
Range100.00001
Interquartile range (IQR)61.525945

Descriptive statistics

Standard deviation34.314884
Coefficient of variation (CV)0.93243973
Kurtosis-1.1492519
Mean36.801182
Median Absolute Deviation (MAD)27.817287
Skewness0.50605174
Sum132741.86
Variance1177.5113
MonotonicityNot monotonic
2024-05-03T20:35:34.550411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 618
 
16.9%
100 114
 
3.1%
25 11
 
0.3%
16.666668 10
 
0.3%
14.285714 9
 
0.2%
33.333336 8
 
0.2%
20 8
 
0.2%
37.5 7
 
0.2%
15.384616 7
 
0.2%
28.571428 7
 
0.2%
Other values (2637) 2808
77.0%
(Missing) 42
 
1.2%
ValueCountFrequency (%)
0 618
16.9%
0.003998721 1
 
< 0.1%
0.011041184 1
 
< 0.1%
0.012061271 1
 
< 0.1%
0.014081532 1
 
< 0.1%
0.01477585 1
 
< 0.1%
0.014817889 1
 
< 0.1%
0.01557875 1
 
< 0.1%
0.016032835 1
 
< 0.1%
0.016329195 1
 
< 0.1%
ValueCountFrequency (%)
100.00001 1
 
< 0.1%
100 114
3.1%
99.99999 5
 
0.1%
99.98112 1
 
< 0.1%
99.97905 1
 
< 0.1%
99.977715 1
 
< 0.1%
99.96099 1
 
< 0.1%
99.933235 1
 
< 0.1%
99.929726 2
 
0.1%
99.92772 1
 
< 0.1%

Primary energy consumption per capita (kWh/person)
Real number (ℝ)

HIGH CORRELATION 

Distinct3628
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25743.982
Minimum0
Maximum262585.7
Zeros21
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:34.692977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile458.07093
Q13116.7373
median13120.57
Q333892.78
95-th percentile103650.43
Maximum262585.7
Range262585.7
Interquartile range (IQR)30776.043

Descriptive statistics

Standard deviation34773.221
Coefficient of variation (CV)1.3507321
Kurtosis8.633884
Mean25743.982
Median Absolute Deviation (MAD)11569.17
Skewness2.6516209
Sum93939789
Variance1.2091769 × 109
MonotonicityNot monotonic
2024-05-03T20:35:34.848887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
0.6%
26508.63 2
 
0.1%
302.59482 1
 
< 0.1%
506.64435 1
 
< 0.1%
294.4527 1
 
< 0.1%
311.80307 1
 
< 0.1%
283.51257 1
 
< 0.1%
275.1137 1
 
< 0.1%
285.46628 1
 
< 0.1%
305.83826 1
 
< 0.1%
Other values (3618) 3618
99.2%
ValueCountFrequency (%)
0 21
0.6%
105.11012 1
 
< 0.1%
105.9355 1
 
< 0.1%
107.3414 1
 
< 0.1%
109.55552 1
 
< 0.1%
110.641914 1
 
< 0.1%
114.514046 1
 
< 0.1%
116.015 1
 
< 0.1%
116.01666 1
 
< 0.1%
122.113625 1
 
< 0.1%
ValueCountFrequency (%)
262585.7 1
< 0.1%
253062.6 1
< 0.1%
245271.83 1
< 0.1%
236185.81 1
< 0.1%
234538.38 1
< 0.1%
232316.58 1
< 0.1%
230796 1
< 0.1%
223248.58 1
< 0.1%
217673.23 1
< 0.1%
216384.27 1
< 0.1%
Distinct1044
Distinct (%)30.3%
Missing207
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean5.3073451
Minimum0.11
Maximum32.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:35.000233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile2.16
Q13.17
median4.3
Q36.0275
95-th percentile11.9285
Maximum32.57
Range32.46
Interquartile range (IQR)2.8575

Descriptive statistics

Standard deviation3.5320201
Coefficient of variation (CV)0.6654966
Kurtosis9.5036572
Mean5.3073451
Median Absolute Deviation (MAD)1.33
Skewness2.5890855
Sum18267.882
Variance12.475166
MonotonicityNot monotonic
2024-05-03T20:35:35.139004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.07 15
 
0.4%
5.15 15
 
0.4%
2.97 14
 
0.4%
3.86 14
 
0.4%
3.18 14
 
0.4%
2.68 13
 
0.4%
4.28 13
 
0.4%
3.57 12
 
0.3%
3.87 12
 
0.3%
3.27 12
 
0.3%
Other values (1034) 3308
90.7%
(Missing) 207
 
5.7%
ValueCountFrequency (%)
0.11 1
 
< 0.1%
0.18 4
0.1%
0.2 1
 
< 0.1%
0.21 2
0.1%
0.22 2
0.1%
0.23 2
0.1%
0.39 1
 
< 0.1%
0.42 1
 
< 0.1%
0.47 2
0.1%
0.49 1
 
< 0.1%
ValueCountFrequency (%)
32.57 1
< 0.1%
29.85 1
< 0.1%
28.2 1
< 0.1%
28.09 1
< 0.1%
27.36 1
< 0.1%
27.27 1
< 0.1%
27.14 1
< 0.1%
26.91 1
< 0.1%
26.15 1
< 0.1%
25.32 1
< 0.1%

Value_co2_emissions_kt_by_country
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2397
Distinct (%)74.4%
Missing428
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean159866.46
Minimum9.9999998
Maximum10707220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:35.293325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9.9999998
5-th percentile180.00001
Q12020
median10500
Q360580.002
95-th percentile462870
Maximum10707220
Range10707210
Interquartile range (IQR)58560.002

Descriptive statistics

Standard deviation773661.06
Coefficient of variation (CV)4.8394207
Kurtosis97.924753
Mean159866.46
Median Absolute Deviation (MAD)10180
Skewness9.3376735
Sum5.1492988 × 108
Variance5.9855144 × 1011
MonotonicityNot monotonic
2024-05-03T20:35:35.467031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.999999776 18
 
0.5%
170.0000018 15
 
0.4%
180.0000072 14
 
0.4%
59.99999866 12
 
0.3%
70.0000003 12
 
0.3%
119.9999973 11
 
0.3%
319.9999928 9
 
0.2%
400.000006 9
 
0.2%
500 9
 
0.2%
129.9999952 9
 
0.2%
Other values (2387) 3103
85.0%
(Missing) 428
 
11.7%
ValueCountFrequency (%)
9.999999776 18
0.5%
10 2
 
0.1%
30 2
 
0.1%
39.99999911 8
0.2%
50 2
 
0.1%
50.00000075 6
 
0.2%
59.99999866 12
0.3%
70.0000003 12
0.3%
79.99999821 5
 
0.1%
80 1
 
< 0.1%
ValueCountFrequency (%)
10707219.73 1
< 0.1%
10502929.69 1
< 0.1%
10096009.77 1
< 0.1%
10006669.92 1
< 0.1%
9984570.313 1
< 0.1%
9874660.156 1
< 0.1%
9861099.609 1
< 0.1%
9541870.117 1
< 0.1%
9282549.805 1
< 0.1%
8474919.922 1
< 0.1%

Renewables (% equivalent primary energy)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1455
Distinct (%)96.2%
Missing2137
Missing (%)58.6%
Infinite0
Infinite (%)0.0%
Mean11.986707
Minimum0
Maximum86.836586
Zeros58
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:35.636318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0040351201
Q12.1370951
median6.290766
Q316.841638
95-th percentile38.898692
Maximum86.836586
Range86.836586
Interquartile range (IQR)14.704543

Descriptive statistics

Standard deviation14.994644
Coefficient of variation (CV)1.2509394
Kurtosis6.0031955
Mean11.986707
Median Absolute Deviation (MAD)5.7403452
Skewness2.2391639
Sum18123.901
Variance224.83936
MonotonicityNot monotonic
2024-05-03T20:35:35.782071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58
 
1.6%
10.885857 1
 
< 0.1%
9.67495 1
 
< 0.1%
11.086973 1
 
< 0.1%
11.174579 1
 
< 0.1%
11.496394 1
 
< 0.1%
11.522516 1
 
< 0.1%
4.3177876 1
 
< 0.1%
11.411279 1
 
< 0.1%
11.174628 1
 
< 0.1%
Other values (1445) 1445
39.6%
(Missing) 2137
58.6%
ValueCountFrequency (%)
0 58
1.6%
7.74 × 10-51
 
< 0.1%
8.17 × 10-51
 
< 0.1%
0.000115751 1
 
< 0.1%
0.000468167 1
 
< 0.1%
0.000582545 1
 
< 0.1%
0.001023664 1
 
< 0.1%
0.00102797 1
 
< 0.1%
0.001917726 1
 
< 0.1%
0.002035834 1
 
< 0.1%
ValueCountFrequency (%)
86.836586 1
< 0.1%
83.54047 1
< 0.1%
83.27399 1
< 0.1%
83.09186 1
< 0.1%
83.07628 1
< 0.1%
82.747856 1
< 0.1%
82.5026 1
< 0.1%
81.80247 1
< 0.1%
81.15162 1
< 0.1%
80.09719 1
< 0.1%

gdp_growth
Real number (ℝ)

MISSING 

Distinct3320
Distinct (%)99.6%
Missing317
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean3.4416101
Minimum-62.07592
Maximum123.13956
Zeros1
Zeros (%)< 0.1%
Negative509
Negative (%)13.9%
Memory size28.6 KiB
2024-05-03T20:35:35.932875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-62.07592
5-th percentile-4.3422707
Q11.3833019
median3.5598552
Q35.8300992
95-th percentile10.2
Maximum123.13956
Range185.21547
Interquartile range (IQR)4.4467973

Descriptive statistics

Standard deviation5.6867202
Coefficient of variation (CV)1.652343
Kurtosis74.772214
Mean3.4416101
Median Absolute Deviation (MAD)2.2205305
Skewness2.5420582
Sum11467.445
Variance32.338786
MonotonicityNot monotonic
2024-05-03T20:35:36.082100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.8 2
 
0.1%
13.2 2
 
0.1%
3.3 2
 
0.1%
0.2 2
 
0.1%
4.200000001 2
 
0.1%
4.1 2
 
0.1%
2.8 2
 
0.1%
3.4 2
 
0.1%
5.9 2
 
0.1%
3 2
 
0.1%
Other values (3310) 3312
90.8%
(Missing) 317
 
8.7%
ValueCountFrequency (%)
-62.07591958 1
< 0.1%
-36.65815267 1
< 0.1%
-36.3919771 1
< 0.1%
-33.4999021 1
< 0.1%
-31.30000005 1
< 0.1%
-30.14513259 1
< 0.1%
-24.00000003 1
< 0.1%
-22.85714286 1
< 0.1%
-21.46426628 1
< 0.1%
-20.59877072 1
< 0.1%
ValueCountFrequency (%)
123.1395552 1
< 0.1%
63.37987543 1
< 0.1%
53.38179418 1
< 0.1%
43.47955594 1
< 0.1%
37.99872686 1
< 0.1%
34.5 1
< 0.1%
33.62937185 1
< 0.1%
30.6122449 1
< 0.1%
27.96153807 1
< 0.1%
26.68090263 1
< 0.1%

gdp_per_capita
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3367
Distinct (%)100.0%
Missing282
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean13283.774
Minimum111.92723
Maximum123514.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.6 KiB
2024-05-03T20:35:36.238080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum111.92723
5-th percentile382.20532
Q11337.8134
median4578.6332
Q315768.615
95-th percentile53860.753
Maximum123514.2
Range123402.27
Interquartile range (IQR)14430.802

Descriptive statistics

Standard deviation19709.867
Coefficient of variation (CV)1.483755
Kurtosis6.1841881
Mean13283.774
Median Absolute Deviation (MAD)3833.8725
Skewness2.3595989
Sum44726468
Variance3.8847885 × 108
MonotonicityNot monotonic
2024-05-03T20:35:36.395897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
464.057999 1
 
< 0.1%
1226.644309 1
 
< 0.1%
1327.966782 1
 
< 0.1%
1499.25762 1
 
< 0.1%
1444.36951 1
 
< 0.1%
1503.864191 1
 
< 0.1%
1655.802946 1
 
< 0.1%
1760.460308 1
 
< 0.1%
1811.636803 1
 
< 0.1%
1934.062922 1
 
< 0.1%
Other values (3357) 3357
92.0%
(Missing) 282
 
7.7%
ValueCountFrequency (%)
111.9272251 1
< 0.1%
113.5672513 1
< 0.1%
119.490396 1
< 0.1%
120.7657837 1
< 0.1%
123.1175361 1
< 0.1%
124.4607909 1
< 0.1%
128.0997081 1
< 0.1%
128.3367028 1
< 0.1%
131.7153072 1
< 0.1%
134.3634269 1
< 0.1%
ValueCountFrequency (%)
123514.1967 1
< 0.1%
119966.0349 1
< 0.1%
119932.2437 1
< 0.1%
118869.2999 1
< 0.1%
117197.4817 1
< 0.1%
117098.4463 1
< 0.1%
116014.6025 1
< 0.1%
113218.7133 1
< 0.1%
113023.1856 1
< 0.1%
112591.1162 1
< 0.1%
Distinct124
Distinct (%)3.4%
Missing1
Missing (%)< 0.1%
Memory size211.9 KiB
2024-05-03T20:35:36.774947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.4470943
Min length1

Characters and Unicode

Total characters8927
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60
2nd row60
3rd row60
4th row60
5th row60
ValueCountFrequency (%)
25 147
 
4.0%
18 113
 
3.1%
4 105
 
2.9%
3 63
 
1.7%
137 63
 
1.7%
83 63
 
1.7%
16 63
 
1.7%
20 63
 
1.7%
17 63
 
1.7%
26 63
 
1.7%
Other values (114) 2842
77.9%
2024-05-03T20:35:37.195108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1618
18.1%
3 1113
12.5%
2 1092
12.2%
4 917
10.3%
6 777
8.7%
5 756
8.5%
0 721
8.1%
8 701
7.9%
7 686
7.7%
9 441
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8822
98.8%
Other Punctuation 105
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1618
18.3%
3 1113
12.6%
2 1092
12.4%
4 917
10.4%
6 777
8.8%
5 756
8.6%
0 721
8.2%
8 701
7.9%
7 686
7.8%
9 441
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1618
18.1%
3 1113
12.5%
2 1092
12.2%
4 917
10.3%
6 777
8.7%
5 756
8.5%
0 721
8.1%
8 701
7.9%
7 686
7.7%
9 441
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1618
18.1%
3 1113
12.5%
2 1092
12.2%
4 917
10.3%
6 777
8.7%
5 756
8.5%
0 721
8.1%
8 701
7.9%
7 686
7.7%
9 441
 
4.9%
Distinct175
Distinct (%)4.8%
Missing1
Missing (%)< 0.1%
Memory size227.6 KiB
2024-05-03T20:35:37.506150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.8333333
Min length2

Characters and Unicode

Total characters24928
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6,52,230
2nd row6,52,230
3rd row6,52,230
4th row6,52,230
5th row6,52,230
ValueCountFrequency (%)
6,52,230 21
 
0.6%
13,878 21
 
0.6%
23,81,741 21
 
0.6%
12,46,700 21
 
0.6%
443 21
 
0.6%
27,80,400 21
 
0.6%
29,743 21
 
0.6%
179 21
 
0.6%
77,41,220 21
 
0.6%
83,871 21
 
0.6%
Other values (165) 3438
94.2%
2024-05-03T20:35:37.957006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 5210
20.9%
0 2877
11.5%
1 2716
10.9%
2 2143
8.6%
4 1850
 
7.4%
8 1820
 
7.3%
3 1807
 
7.2%
7 1785
 
7.2%
6 1688
 
6.8%
5 1617
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19718
79.1%
Other Punctuation 5210
 
20.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2877
14.6%
1 2716
13.8%
2 2143
10.9%
4 1850
9.4%
8 1820
9.2%
3 1807
9.2%
7 1785
9.1%
6 1688
8.6%
5 1617
8.2%
9 1415
7.2%
Other Punctuation
ValueCountFrequency (%)
, 5210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24928
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 5210
20.9%
0 2877
11.5%
1 2716
10.9%
2 2143
8.6%
4 1850
 
7.4%
8 1820
 
7.3%
3 1807
 
7.2%
7 1785
 
7.2%
6 1688
 
6.8%
5 1617
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 5210
20.9%
0 2877
11.5%
1 2716
10.9%
2 2143
8.6%
4 1850
 
7.4%
8 1820
 
7.3%
3 1807
 
7.2%
7 1785
 
7.2%
6 1688
 
6.8%
5 1617
 
6.5%

Latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct175
Distinct (%)4.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean18.246388
Minimum-40.900557
Maximum64.963051
Zeros0
Zeros (%)0.0%
Negative798
Negative (%)21.9%
Memory size28.6 KiB
2024-05-03T20:35:38.124978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-40.900557
5-th percentile-23.442503
Q13.202778
median17.189877
Q338.969719
95-th percentile55.378051
Maximum64.963051
Range105.86361
Interquartile range (IQR)35.766941

Descriptive statistics

Standard deviation24.159232
Coefficient of variation (CV)1.3240556
Kurtosis-0.61488052
Mean18.246388
Median Absolute Deviation (MAD)17.936536
Skewness-0.24218556
Sum66562.823
Variance583.66848
MonotonicityNot monotonic
2024-05-03T20:35:38.279080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.93911 21
 
0.6%
21.4735329 21
 
0.6%
52.132633 21
 
0.6%
-20.904305 21
 
0.6%
-40.900557 21
 
0.6%
12.865416 21
 
0.6%
17.607789 21
 
0.6%
9.081999 21
 
0.6%
41.608635 21
 
0.6%
60.472024 21
 
0.6%
Other values (165) 3438
94.2%
ValueCountFrequency (%)
-40.900557 21
0.6%
-38.416097 21
0.6%
-35.675147 21
0.6%
-32.522779 21
0.6%
-30.559482 21
0.6%
-29.609988 21
0.6%
-26.522503 21
0.6%
-25.274398 21
0.6%
-23.442503 21
0.6%
-22.95764 21
0.6%
ValueCountFrequency (%)
64.963051 21
0.6%
61.92411 21
0.6%
60.472024 21
0.6%
60.128161 21
0.6%
58.595272 21
0.6%
56.879635 21
0.6%
56.26392 21
0.6%
56.130366 21
0.6%
55.378051 21
0.6%
55.169438 21
0.6%

Longitude
Real number (ℝ)

Distinct175
Distinct (%)4.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14.822695
Minimum-175.19824
Maximum178.06503
Zeros0
Zeros (%)0.0%
Negative1155
Negative (%)31.7%
Memory size28.6 KiB
2024-05-03T20:35:38.429987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-175.19824
5-th percentile-88.49765
Q1-11.779889
median19.145136
Q346.199616
95-th percentile138.25292
Maximum178.06503
Range353.26327
Interquartile range (IQR)57.979505

Descriptive statistics

Standard deviation66.348148
Coefficient of variation (CV)4.4761192
Kurtosis0.43573263
Mean14.822695
Median Absolute Deviation (MAD)28.574635
Skewness-0.033539968
Sum54073.19
Variance4402.0767
MonotonicityNot monotonic
2024-05-03T20:35:38.569978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.709953 21
 
0.6%
55.975413 21
 
0.6%
5.291266 21
 
0.6%
165.618042 21
 
0.6%
174.885971 21
 
0.6%
-85.207229 21
 
0.6%
8.081666 21
 
0.6%
8.675277 21
 
0.6%
21.745275 21
 
0.6%
8.468946 21
 
0.6%
Other values (165) 3438
94.2%
ValueCountFrequency (%)
-175.198242 21
0.6%
-172.104629 21
0.6%
-157.3768317 21
0.6%
-106.346771 21
0.6%
-102.552784 21
0.6%
-95.712891 21
0.6%
-90.230759 21
0.6%
-88.89653 21
0.6%
-88.49765 21
0.6%
-86.241905 21
0.6%
ValueCountFrequency (%)
178.065032 21
0.6%
177.64933 21
0.6%
174.885971 21
0.6%
166.959158 21
0.6%
166.931503 21
0.6%
165.618042 21
0.6%
160.156194 21
0.6%
143.95555 21
0.6%
138.252924 21
0.6%
133.775136 21
0.6%

Interactions

2024-05-03T20:35:26.685095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:40.362365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:43.528072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:46.247323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:48.984197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:51.805263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:54.726219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:57.447360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:00.306500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:03.097330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:05.678932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:08.062438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:10.734986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:13.486257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:16.266458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:18.965218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:21.418091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:24.059906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:26.825249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:40.693420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:43.682229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:46.404501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:49.139943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:51.955024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:54.873093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:57.600155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:00.513312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:03.252983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:05.822066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:08.320486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:10.899931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:13.635052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:16.423010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:19.149234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:21.554034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:24.204922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:26.957276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:40.834243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:43.818985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:46.549314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:49.280446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:52.097956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:55.005987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:57.736202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:00.646791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:03.387022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:05.947969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:08.454197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:11.045098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:13.776007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:16.582129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:19.280181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:21.677409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:24.344933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:27.120062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:40.973425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:43.965883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:46.689314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:49.422295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:52.231522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:55.152076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:58.003073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:00.816534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:03.513029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:06.070447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:08.579204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:11.177294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:13.909265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:16.729954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:19.409392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:21.805506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:24.482469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:27.309506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:41.144984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:44.105230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:46.840404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:49.569326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:52.375568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:55.304312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:58.145364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:00.975442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:03.645155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:06.205013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:08.713093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:11.320171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:14.045966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:16.888864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:19.542960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:22.042195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:24.620381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:27.482510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:41.297947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:44.298195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:46.987262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:49.738599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:52.518909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:55.443162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:58.289076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:01.128444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:03.792090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:06.348024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:08.849087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:11.447318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:14.191924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:17.052355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:19.675901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:22.185310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:24.754931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:27.623235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:41.444941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:44.438998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:47.268356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:49.881816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:52.667902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:55.581182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:58.443023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:01.299373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:03.931548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:06.480364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:08.999405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:11.685187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:14.357972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:17.265299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:19.813979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:22.326144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:24.921334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2024-05-03T20:35:12.305930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:14.996910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:17.811033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:20.369141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:22.920457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2024-05-03T20:35:23.062312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:25.736351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2024-05-03T20:34:42.459372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:45.371480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:48.178724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:50.910368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:53.737611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:56.531306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:59.337432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:02.232330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:04.746072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:07.270035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:09.812414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:12.615479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:15.439181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:18.073654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:20.633954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:23.222340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:25.869449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:28.585094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:42.608346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:45.510409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:48.310400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:51.049255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2024-05-03T20:35:02.366494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2024-05-03T20:35:07.409958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:09.943402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:12.763027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:15.571360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:18.202167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:20.770165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:23.369275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:26.012262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:28.819312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:42.770292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:45.660185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:48.443522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:51.235133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:54.032128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:56.823064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:59.618994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:02.491972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:05.145041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:07.535401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:10.079894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:12.906097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:15.703260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:18.321243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:20.890086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:23.508917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:26.140304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:28.948183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:42.923968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:45.839280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:48.576099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:51.393162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:54.172205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:56.978131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:59.762162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:02.651301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:05.300153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:07.668044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:10.219071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:13.080162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:15.845257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:18.443340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:21.029294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:23.646792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:26.292303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:29.077931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:43.116093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:45.980135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:48.702452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:51.529064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:54.445081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:57.176127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:59.902185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:02.824891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:05.431330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:07.799044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:10.419236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:13.209010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:15.981235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:18.603579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:21.163831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:23.782338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:26.424936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:29.197926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:43.258255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:46.113015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:48.841134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:51.661098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:54.583508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:34:57.305977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:00.095355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:02.955014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:05.553850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:07.927939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:10.590822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:13.339172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:16.106271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:18.843279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:21.288954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:23.925360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-03T20:35:26.553077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2024-05-03T20:35:38.728264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Access to clean fuels for cookingAccess to electricity (% of population)Electricity from fossil fuels (TWh)Electricity from nuclear (TWh)Electricity from renewables (TWh)Energy intensity level of primary energy (MJ/$2017 PPP GDP)Financial flows to developing countries (US $)LatitudeLongitudeLow-carbon electricity (% electricity)Primary energy consumption per capita (kWh/person)Renewable energy share in the total final energy consumption (%)Renewable-electricity-generating-capacity-per-capitaRenewables (% equivalent primary energy)Value_co2_emissions_kt_by_countryYeargdp_growthgdp_per_capita
Access to clean fuels for cooking1.0000.8940.5470.3480.264-0.2600.0740.512-0.091-0.0990.912-0.7270.252-0.0130.5540.057-0.2240.899
Access to electricity (% of population)0.8941.0000.5450.3490.285-0.2170.1410.610-0.015-0.0800.878-0.6950.311-0.0240.5330.130-0.2200.843
Electricity from fossil fuels (TWh)0.5470.5451.0000.4950.521-0.0300.2950.4270.122-0.1590.530-0.4990.185-0.2550.9430.067-0.0800.474
Electricity from nuclear (TWh)0.3480.3490.4951.0000.4960.0710.2630.386-0.0470.2150.363-0.1840.2310.1470.545-0.007-0.1170.313
Electricity from renewables (TWh)0.2640.2850.5210.4961.000-0.0420.3610.2640.0040.6560.2160.1380.7560.7220.6460.122-0.0690.227
Energy intensity level of primary energy (MJ/$2017 PPP GDP)-0.260-0.217-0.0300.071-0.0421.000-0.0040.0090.2330.053-0.1080.242-0.176-0.390-0.011-0.1610.149-0.374
Financial flows to developing countries (US $)0.0740.1410.2950.2630.361-0.0041.0000.1250.0820.0790.057-0.0120.1500.1370.3240.2380.0360.067
Latitude0.5120.6100.4270.3860.2640.0090.1251.000-0.0200.0130.499-0.343-0.1010.1510.4480.003-0.0610.405
Longitude-0.091-0.0150.122-0.0470.0040.2330.082-0.0201.000-0.076-0.019-0.060-0.089-0.3590.0600.0010.211-0.126
Low-carbon electricity (% electricity)-0.099-0.080-0.1590.2150.6560.0530.0790.013-0.0761.000-0.1240.5030.6490.8490.0110.061-0.011-0.119
Primary energy consumption per capita (kWh/person)0.9120.8780.5300.3630.216-0.1080.0570.499-0.019-0.1241.000-0.7100.266-0.1720.5420.027-0.1900.900
Renewable energy share in the total final energy consumption (%)-0.727-0.695-0.499-0.1840.1380.242-0.012-0.343-0.0600.503-0.7101.0000.0370.793-0.409-0.0050.130-0.651
Renewable-electricity-generating-capacity-per-capita0.2520.3110.1850.2310.756-0.1760.150-0.101-0.0890.6490.2660.0371.0000.8200.2590.206-0.0460.253
Renewables (% equivalent primary energy)-0.013-0.024-0.2550.1470.722-0.3900.1370.151-0.3590.849-0.1720.7930.8201.000-0.1290.168-0.2020.149
Value_co2_emissions_kt_by_country0.5540.5330.9430.5450.646-0.0110.3240.4480.0600.0110.542-0.4090.259-0.1291.0000.064-0.0530.463
Year0.0570.1300.067-0.0070.122-0.1610.2380.0030.0010.0610.027-0.0050.2060.1680.0641.000-0.2180.206
gdp_growth-0.224-0.220-0.080-0.117-0.0690.1490.036-0.0610.211-0.011-0.1900.130-0.046-0.202-0.053-0.2181.000-0.284
gdp_per_capita0.8990.8430.4740.3130.227-0.3740.0670.405-0.126-0.1190.900-0.6510.2530.1490.4630.206-0.2841.000

Missing values

2024-05-03T20:35:29.404128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T20:35:29.764303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-03T20:35:30.259935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

EntityYearAccess to electricity (% of population)Access to clean fuels for cookingRenewable-electricity-generating-capacity-per-capitaFinancial flows to developing countries (US $)Renewable energy share in the total final energy consumption (%)Electricity from fossil fuels (TWh)Electricity from nuclear (TWh)Electricity from renewables (TWh)Low-carbon electricity (% electricity)Primary energy consumption per capita (kWh/person)Energy intensity level of primary energy (MJ/$2017 PPP GDP)Value_co2_emissions_kt_by_countryRenewables (% equivalent primary energy)gdp_growthgdp_per_capitaDensity\n(P/Km2)Land Area(Km2)LatitudeLongitude
0Afghanistan20001.6135916.209.2220000.044.990.160.00.3165.957440302.594821.64760.000000NaNNaNNaN606,52,23033.9391167.709953
1Afghanistan20014.0745747.208.86130000.045.600.090.00.5084.745766236.891851.74730.000000NaNNaNNaN606,52,23033.9391167.709953
2Afghanistan20029.4091588.208.473950000.037.830.130.00.5681.159424210.862151.401029.999971NaNNaN179.426579606,52,23033.9391167.709953
3Afghanistan200314.7385069.508.0925970000.036.660.310.00.6367.021280229.968221.401220.000029NaN8.832278190.683814606,52,23033.9391167.709953
4Afghanistan200420.06496810.907.75NaN44.240.330.00.5662.921350204.231251.201029.999971NaN1.414118211.382074606,52,23033.9391167.709953
5Afghanistan200525.39089412.207.519830000.033.880.340.00.5963.440857252.069121.411549.999952NaN11.229715242.031313606,52,23033.9391167.709953
6Afghanistan200630.71869013.857.4010620000.031.890.200.00.6476.190475304.420901.501759.999990NaN5.357403263.733602606,52,23033.9391167.709953
7Afghanistan200736.05101015.307.2515750000.028.780.200.00.7578.947370354.279901.531769.999981NaN13.826320359.693158606,52,23033.9391167.709953
8Afghanistan200842.40000016.707.4916170000.021.170.190.00.5473.972600607.833501.943559.999943NaN3.924984364.663542606,52,23033.9391167.709953
9Afghanistan200946.74005018.407.509960000.016.530.160.00.7882.978720975.048162.254880.000114NaN21.390528437.268740606,52,23033.9391167.709953
EntityYearAccess to electricity (% of population)Access to clean fuels for cookingRenewable-electricity-generating-capacity-per-capitaFinancial flows to developing countries (US $)Renewable energy share in the total final energy consumption (%)Electricity from fossil fuels (TWh)Electricity from nuclear (TWh)Electricity from renewables (TWh)Low-carbon electricity (% electricity)Primary energy consumption per capita (kWh/person)Energy intensity level of primary energy (MJ/$2017 PPP GDP)Value_co2_emissions_kt_by_countryRenewables (% equivalent primary energy)gdp_growthgdp_per_capitaDensity\n(P/Km2)Land Area(Km2)LatitudeLongitude
3639Zimbabwe201136.90000030.166.201080000.079.273.430.05.5161.6331143860.789811.7711409.99985NaN14.1939131093.653409383,90,757-19.01543829.154857
3640Zimbabwe201244.00000029.865.14NaN77.503.250.05.6863.6058204106.949710.4212010.00023NaN16.6654291304.968011383,90,757-19.01543829.154857
3641Zimbabwe201340.49837529.864.44319000000.078.873.900.05.3657.8833704085.332010.4812279.99973NaN1.9894931429.998461383,90,757-19.01543829.154857
3642Zimbabwe201432.30000029.563.3817830000.080.273.920.05.7959.6292503940.886210.4012079.99992NaN2.3769291434.896277383,90,757-19.01543829.154857
3643Zimbabwe201533.70000029.563.54NaN80.824.020.05.3757.1884963860.920210.3612430.00031NaN1.7798731445.069702383,90,757-19.01543829.154857
3644Zimbabwe201642.56173029.862.8830000.081.903.500.03.3248.6803503227.680210.0011020.00046NaN0.7558691464.588957383,90,757-19.01543829.154857
3645Zimbabwe201744.17863529.862.335570000.082.463.050.04.3058.5034073068.01159.5110340.00015NaN4.7094921235.189032383,90,757-19.01543829.154857
3646Zimbabwe201845.57264729.982.5310000.080.233.730.05.4659.4124073441.98589.8312380.00011NaN4.8242111254.642265383,90,757-19.01543829.154857
3647Zimbabwe201946.78147530.181.40250000.081.503.660.04.5855.5825273003.655310.4711760.00023NaN-6.1442361316.740657383,90,757-19.01543829.154857
3648Zimbabwe202052.74767030.480.6130000.081.903.400.04.1955.2042162680.131810.00NaNNaN-6.2487481214.509820383,90,757-19.01543829.154857